Analyzing multiple endpoints in clinical trials of pain treatments: IMMPACT recommendations
Bibliographic record
Abstract
The increasing complexity of randomized clinical trials and the practice of obtaining a wide variety of measurements from study participants have made the consideration of multiple endpoints a critically important issue in the design, analysis, and interpretation of clinical trials. Failure to consider important outcomes can limit the validity and utility of clinical trials; specifying multiple endpoints for the evaluation of treatment efficacy, however, can increase the rate of false positive conclusions about the efficacy of a treatment. We describe the use of multiple endpoints in the design, analysis, and interpretation of pain clinical trials, and review available strategies and methods for addressing multiplicity. To decrease the probability of a Type I error (i.e., the likelihood of obtaining statistically significant results by chance) in pain clinical trials, the use of gatekeeping procedures and other methods that correct for multiple analyses is recommended when a single primary endpoint does not adequately reflect the overall benefits of treatment. We emphasize the importance of specifying in advance the outcomes and clinical decision rule that will serve as the basis for determining that a treatment is efficacious and the methods that will be used to control the overall Type I error rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.306 | 0.476 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".